Systems and methods for predictive modeling via simulation

ABSTRACT

Methods, systems, and computer readable media for predictively determining a risk of damage to a property are provided. To determine the risk, a high resolution virtual model of a region that includes the property is obtained. The virtual model is imported into a simulation environment. One or more of the simulation parameters are set based on historic weather data for the region. For example, each parameter may be associated with a probability distribution derived based on the historic weather data that is sampled prior to executing the simulation. One or more simulations are executed in accordance with the sampled inputs to simulate the likely weather patterns the property will experience. The result of the simulation is analyzed to determine the predicted risk of damage to the property.

FIELD OF THE DISCLOSURE

The present disclosure relates to modeling risk of damage to properties,and, in particular, to modeling the risk of damage based upon simulationacting upon a virtual model of the property.

BACKGROUND

Traditionally, underwriters analyze historical data to understand theirrisks. For example, an underwriter may analyze an average annualrainfall to estimate a likelihood that the property floods. However,such techniques can only generally determine a risk associated with aregion, not a risk that is particular to a specific property.

In some prior attempts to solve this problem, underwriters have lookedto aerial imagery to better understand the characteristics of differentproperties. While the aerial imagery techniques traditionally reliedupon are able to generally provide an indication of a type of property(e.g., residential, commercial, etc.) and/or the presence of vegetation,the aerial images do not provide sufficient resolution to determineunique characteristics associated with different properties.Accordingly, traditional techniques of assess risk based on generalassumptions about the property, rather than the particularcharacteristics of an individual property. For example, whileneighboring residential properties may have different risks due todifferent types of vegetation having a varying capacity to absorb waterand/or different topographical positioning with respect to a runoffsystem, the traditional techniques would identically assess theseneighboring residential properties based on the historic data. As aresult, these traditional solutions that rely upon aerial imagery do notaccurately reflect the true risk of damage associated with a property.

SUMMARY

In one aspect, a computer-implemented method is provided. The method mayinclude (1) identifying, by one or more processors, a virtual model of aregion, the virtual model being generated based upon a plurality ofimages captured by a remote imaging vehicle, wherein the virtual modelmodels the region to an accuracy of at least 10 cm and includescomponent virtual models of a plurality of properties in the region; (2)importing, by one or more processors, the virtual model of the regioninto a simulation environment; (3) based on a plurality of historicalweather data associated with the region, setting, by the one or moreprocessors, one or more parameters of the simulation environment,wherein the one or more parameters model a weather system; (4) inaccordance with the one or more parameters, executing, by the one ormore processors, a simulation where the weather system acts upon thevirtual model of the region; (5) analyzing, by the one or moreprocessors, a result of the simulation to determine a risk of damage toa component virtual model of a property in the region during thesimulation; and (6) updating, by the one or more processors, a recordcorresponding to the property to indicate the risk of damage to thevirtual model of the property during the simulation.

In another aspect, a system is provided. The system may include (i) oneor more processors; (ii) one or more transceivers operatively connectedto the one or more processors and configured to send and receivecommunications over one or more communication networks; and (iii) one ormore non-transitory memories coupled to the one or more processors andstoring computer-executable instructions, that, when executed by the oneor more processors, cause the system to (1) identify a virtual model ofa region, the virtual model being generated based upon a plurality ofimages captured by a remote imaging vehicle, wherein the virtual modelmodels the region to an accuracy of at least 10 cm and includescomponent virtual models of a plurality of properties in the region; (2)import the virtual model of the region into a simulation environment;(3) based on a plurality of historical weather data associated with theregion, set one or more parameters of the simulation environment,wherein the one or more parameters model a weather system; (4) inaccordance with the one or more parameters, execute a simulation wherethe weather system acts upon the virtual model of the region; (5)analyze a result of the simulation to determine a risk of damage to acomponent virtual model of a property in the region during thesimulation; and (6) update a record corresponding to the property toindicate the risk of damage to the virtual model of the property duringthe simulation.

In yet another aspect, a non-transitory computer-readable medium storingcomputer-executable instructions is provided. The instructions, whenexecuted by one or more processors, cause one or more processors to (1)identify a virtual model of a region, the virtual model being generatedbased upon a plurality of images captured by a remote imaging vehicle,wherein the virtual model models the region to an accuracy of at least10 cm and includes component virtual models of a plurality of propertiesin the region; (2) import the virtual model of the region into asimulation environment; (3) based on a plurality of historical weatherdata associated with the region, set one or more parameters of thesimulation environment, wherein the one or more parameters model aweather system; (4) in accordance with the one or more parameters,execute a simulation where the weather system acts upon the virtualmodel of the region; (5) analyze a result of the simulation to determinea risk of damage to a component virtual model of a property in theregion during the simulation; and (6) update a record corresponding tothe property to indicate the risk of damage to the virtual model of theproperty during the simulation.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an example environment for predictively modeling risksassociated with a property.

FIG. 2 depicts an example environment wherein an imaging vehiclecaptures a set of image data representative of a region.

FIG. 3 depicts an example virtual model of the region.

FIG. 4 depicts a block diagram for generating a predictive model for oneor more parameters of a simulation environment.

FIG. 5 depicts a block diagram for predictively simulating the impact ofweather on the region and a corresponding simulation result.

FIG. 6 depicts flow chart of an example method for predictively modelingrisks associated with properties within a region.

FIG. 7 depicts a block diagram of an example prediction server.

DETAILED DESCRIPTION

Methods, systems, and computer readable media for predictively modelingrisks associated with a property are described herein. Moreparticularly, the present disclosure relates to predictively simulatingweather conditions acting upon a “digital twin” of the property todetermine a risk of damage to the property due to weather events. As itis generally used herein, a digital twin refers to a high resolutionvirtual model of a physical property. As it is generally used herein,“high resolution” refers to the virtual model modeling the physicalproperty to an accuracy on the scale of at least 10 cm. That is, theunderlying image data that forms the basis of the virtual model canrepresent portions of the region that are 10 cm or less apart from oneanother. In some embodiments, the high resolution model accuratelymodels the region on the scale of 1 cm, 100 mm, or less. Accordingly,when the virtual model is imported into a simulation environment, thesimulation acts upon an accurate representation of the region.

In some embodiments, the virtual model is more than just a visuallyaccurate representation of the property, but also one that accuratelymodels the structural properties of the property. As one example, thesystem may identify and simulate the performance of a ground materialcovering a surface of the region (e.g., asphalt, gravel, grass and/orparticular species thereof, sand, mud, and so on). In this example, thesimulation application models the ability of the different materials toabsorb water. As another example, structural properties may beassociated with a building information modeling (BIM) representationthat identifies the component materials used to construct the structure.For example, the BIM of a property may indicate the particular type ofmaterial used to support the structure (e.g., the material of theinternal structural components such as columns, struts, frames, etc.)and/or external surface material (e.g., a siding material, a roofingmaterial, a window material, etc.). Based on the particular materials,the simulation application may model the structure's resistivity toweather events and/or any long-term erosive effects caused thereby. Insome embodiments, the high resolution image data enables the externalmaterials to be identified and/or classified via image analysistechniques.

In addition to accurately modeling the behavior of the properties withinthe region when acted upon by a weather event, the techniques describeherein also predictively model the weather events expected to act uponthe property. To this end, traditional modeling approaches rely on data,such as an average rainfall, that assumes all properties withinparticular regions experience the same amount of precipitation during astorm. However, this data does not describe the true distribution ofwater during a storm, in particular, with respect to water flows.Instead, techniques disclosed herein, simulate weather events to modelhow storms actually impact an environment. For example, the simulationmay model wind patterns, storm intensities, storm densities,precipitation type, and/or other weather characteristics to accuratelysimulate how the storm impacts each specific part of the regiondifferently.

In some embodiments, each of these weather characteristics areassociated with probability distribution that models an intensity and/orlikelihood of occurrence associated with each weather characteristic.For example, maximum wind speed may be modeled on a scale of 0-160 milesper hour; whereas, a probability of a tornado occurring may be modeledon a scale of probabilities from 0-1. Techniques described herein maydevelop the various probability distributions based on historical dataand may continuously update the probability distributions as new data isobtained. Accordingly, to accurately assess the weather risk over time,techniques described herein execute a plurality of simulations (e.g.,100 simulations, 1000 simulations, 5000 simulations, 10000 simulations,and so on) each sampling the various probability distributions tosimulate the most likely weather events to occur in the region.

Consequently, the techniques described herein predictively model boththe expected types of weather experienced by the region and the abilityof the property to withstand the expected weather to accurately model arisk of damage to the property. As a result, the risk of damage to aspecific property can be accurately predicted and distinguished from therisk of damage to other, similarly-situated properties.

Turning to FIG. 1, illustrated is an example environment 100 forpredictively modeling risks associated with a property. Although FIG. 1depicts certain entities, components, and devices, it should beappreciated that additional or alternate entities and components areenvisioned.

As illustrated, the environment 100 may include a client device 110. Theclient device 110 may be any type of electronic device, such as adesktop computer, a laptop, a tablet, a smartphone, a phablet, a smartwatch, smart glasses, wearable electronics, pager, personal digitalassistant, and/or any other electronic device, including computingdevices configured for wireless radio frequency (RF) communication. Theclient device 110 may store and execute an application that enables theclient device to interface with a prediction server 120 to perform thepredictive modeling techniques described herein. More particularly, theclient device 110 may communicate with the prediction server 120 via oneor more networks 115. The networks 115 may facilitate any type of datacommunication via any standard or technology (e.g., GSM, CDMA, TDMA,WCDMA, LTE, EDGE, OFDM, GPRS, EV-DO, UWB, IEEE 802 including Ethernet,WiMAX, WiFi, Bluetooth, and others) or any combination thereof.

The prediction server 120 may store a set of processor-executableinstructions that enable a user of the client device 110 and/or theprediction server 120 to predictively determine a risk of damageassociated with a property. In some embodiments, the prediction server120 is not a single server, but a distributed and/or cloud computingplatform that includes any number of servers. For ease explanation, thepresent disclosure occasionally refers the prediction server 120 as asingle server; however, as generally used herein, the term “server” inits singular form encompasses a network of different servers interactingwith one another as part of a distributing computing environment.

As illustrated, the prediction sever 120 is operatively coupled to animage database 132 configured to store image data of a region, a modeldatabase 134 configured to store virtual models (including BIM models)of properties located in the region, a weather database 136 configuredto store historical weather data for the region and probabilitydistributions derived from the historical weather data, and a propertydatabase 138 configured to store information relating the propertieslocated in the region. Although FIG. 1 depicts the databases 132-138 ascoupled to the prediction server 120, it is envisioned that any of thedatabase 132-138 may be maintained in the “cloud” such that any elementof the environment 100 capable of communicating over the network 115 maydirectly interact with the databases 132-138. In some embodiments, anyof the database 132-138 may be stored in a memory component of theprediction server 120 and/or the client device 110.

Generally, the functionality associated predictive modeling techniquesdescribed herein are divided between the client device 110 and theprediction server 120. In some embodiments, because the predictionserver 120 generally has more powerful processing capabilities, theclient device 110 may store and execute a light-weight application thatenables the user of the client device 110 to control how the predictionserver 120 executes a simulation. That is, in these embodiments, theclient device 110 is configured to communicate control parameters to theprediction server 120 via the network 115 and the prediction server 120interprets the control parameters to execute the simulations. Theprediction server 120 may then transmit data and/or visual outputs tothe client device 110 via the network 115 to enable the user of theclient device 110 to view the result of the simulation.

In other embodiments, the client device 110 is configured to perform thesimulation. In these embodiments, the client device 110 may periodicallysynchronize local versions of the databases 132-138 with a version ofthe databases 132-138 maintain by the prediction server 120. Forexample, over time, the prediction server 120 may obtain new historicalweather data and update the probability distributions associated withone or more weather events. Accordingly, the client device 110 may beconfigured to poll the prediction server 120 upon launching the clientapplication to determine whether the weather database 136 has beenupdated by the prediction server 120. As another example, the predictionserver 120 may dispatch one or more imaging vehicles to capture imagedata of the region to update the models of the properties within theregion. Accordingly, the client device 110 may be configured to poll theprediction server 120 to determine whether the model database 134 hasbeen updated by the prediction server 120.

Turning to FIG. 2, illustrated is an example environment 200 wherein animaging vehicle 240 captures a set of image data representative of aregion 205. As illustrated, the environment 200 includes an imagingvehicle 240 one or more image sensors 242 configured to capture imagedata while traversing the region 205. The region 205 may include aplurality of properties, such as structures (e.g., a house, building,silo, billboard, or other structures), vegetation (e.g., farm land,forests, prairie, grassland, and so on), throughways (roads, paths,trails), and/or any other type of property. Although FIG. 2 only depictsa single imaging vehicle 240, in other embodiments multiple imagingvehicles 240 may be used to capture the set of image data.

As described herein, the image sensors 242 are configured to capturehigh-resolution image data representative of the region 205.Accordingly, the image sensors 242 may be configured to capture imagedata at a sufficient resolution to represent the region 205 at anaccuracy with 10 cm. To achieve this resolution, the imaging vehicle 240may be configured to traverse the region below a particular altitudethereby capturing the image data from a position closer to theparticular properties within the region 205. According to aspects, colorinformation is useful in determining the particular material and/or typeof vegetation in the environment. Accordingly, the image sensors 242 maybe configured to capture color image data representative of the region205.

According to certain aspects, the imaging vehicle 240 may be manually orautonomously piloted. The imaging sensors 242 may include be associatedwith a field of imaging 243 indicative of the particular portion of theregion represented by the captured set of image data. As the imagingvehicle 240 traverses the region 205, the field of imaging 243 alsomoves. Accordingly, the imaging vehicle 240 may capture imaging dataindicative of the different portions of the overall region 205. Itshould be appreciated that in some embodiments, the field of imaging 243is not at a fixed angle below the imaging vehicle 240, but may pan,tilt, and/or zoom to capture image data indicative of the region 205 atdifferent angles. In some implementations, the imaging vehicle 240captures image data such that there is an overlap between successivesets of captured image data. These overlaps provide additional imagedata about the same location of the region 205, which enables moreaccurate determination of the dimensions of features (e.g., structures,trees, roads, water, and so on) of the region 205.

The imaging vehicle 240 may also include a communication apparatus fortransmitting, via a communication network 225, the captured sets ofimage data to a server 220 (such as the prediction server 120 of FIG.1). The communication network 225 may support communications via anystandard or technology (e.g., GSM, CDMA, TDMA, WCDMA, LTE, EDGE, OFDM,GPRS, EV-DO, UWB, IEEE 802 including Ethernet, WiMAX, and/or others).The server 220 may store any received image data at an image database234 (such as the image database 134 of FIG. 1).

According to aspects, the server 220 may analyze the image data storedat the image database 234 to generate virtual models of the region 205and/or the various properties therein. To generate a virtual model, theserver 220 may analyze the image data to determine dimensions for thevarious properties of the region 205 and/or to adapt the image data toappear on the appropriate dimension of each property. In someimplementations, the server 220 generates a virtual model for aplurality of the properties of the region 205. Accordingly, the virtualmodel for the region 205 may include several virtual models of thevarious properties within the region 205. The server 220 may then storethe generated virtual models at a model database 234 (such as the modeldatabase 134 of FIG. 1). In other embodiments, to obtain the virtualmodel of a structure, the server 220 analyzes the image data stored atthe image database 232 to determine an address associated with thestructure. Based on the determined address, the server 220 queries a BIMdatabase (not depicted) to obtain a BIM model of the structure.

FIG. 3 illustrates an example virtual model 340 of a region (such as theregion 205 of FIG. 2). For example the virtual model 340 may begenerated by the prediction servers 120 or 220 in response to obtainingimage data of the region. As illustrated, the virtual model 340 includescomponent models of various properties included within the modeledregion. For example, the virtual model 340 includes a model of the homes342 a, 342 b, and 342 c, a model of the trees 342 d, a model of theretention pond 342 e, and a model of the road 342 f.

As described herein, the models 342 do not merely reflect the visualappearance of the corresponding property, but also the behavior of thevarious materials to comprise the corresponding properties. For example,based on an analysis of the image data stored in an image database (suchas the image database 132 of FIG. 1), the prediction server may identifythe tree modeled by the model 342 d as an oak tree and associate themodel 342 d with a set of properties (e.g., a trunk strength, a rootgrowth radius, a water absorptiveness, etc.) corresponding to oak trees.Similarly, the prediction server may identify a water holding capacityof the retention pond modeled by the model 342 e to identify an amountof rain water and/or runoff that can be contained within the retentionpond before overflowing.

Of course, the specific properties identified in FIG. 3 are merelyexample properties that are identified and modeled by the predictionserver. In implementations, the prediction server may model as manyproperties as possible to provide the most accurate representation ofthe region to more accurately predict the ability of the properties towithstand weather events. Accordingly, various embodiments includemodeling unlabeled aspects of the virtual model 340, such as the variousgrasses, roadways, farm lands, and so on also included in virtual model340. In addition to the material that forms the property, the topographyof the property is also modeled. That is, for properties that includeyards, farmland, grassland, or other outdoor spaces, the specifictopography is modeled. As a result, the simulation software can modeland predict the flow of water throughout the region's topography duringa weather event.

Turning now to FIG. 4, illustrated is a block diagram of an exampleprediction server (such as the prediction server 120 of FIG. 1)generating a predictive model for one or more parameters of a simulationenvironment. More particularly, FIG. 4 illustrates a modeling engine 424of the prediction server generating a predictive model for a set ofinputs that that relate to how weather acts upon a virtual model of aregion in a simulation environment.

As illustrated, the prediction server begins by analyzing historicweather data 436 stored in a weather database (such as the weatherdatabase 136 of FIG. 1). For example, the weather data 436 may indicateflood and/or storm recurrence probabilities (such as those maintained bythe United States Geological Survey (USGS)), water level data, waterdischarge rates, historical stream flow data, and/or precipitationfrequency estimates (such as those maintained by the National Oceanicand Atmospheric Administration (NOAA)) for various precipitationintensities. The weather data 436 may also be associated with ageographic location. Accordingly, when predicting the risk associatedwith a specific property, the prediction server may obtain weather datathat is associated with a geographic location proximate to thegeographic location to the property.

Additionally, the prediction server utilizes a modeling engine 424 toconvert the historic weather data 436 into one or more predictivemodels. More particularly, the prediction server may determine one ormore input parameters associated with weather events in the simulationenvironment of a simulation application and generate one or moreprobability distributions for the input parameters. For example, a firstparameter may correspond to the probability of a body of waterexperiencing a 5 year flood, a second parameter may correspond to anintensity of rainfall, and a third parameter may correspond to an amountof rainfall. Of course, any number of parameters associated with thesimulation environment can be modeled by the modeling engine 424.

After identifying the particular parameters to model, the predictionserver may then generate a probability distribution 438 for each of theparameters. For some parameters, the probability distribution may beobtained via the historic weather data 436. For example, if the historicweather data 436 includes a storm recurrence probability for storms of aparticular intensity, the modeling engine 424 may utilize thisprobability to define the probability distribution 438 for a parameterassociated with a presence of a storm of the particular intensity. Forother parameters, the modeling engine 424 analyzes the historic weatherdata 436 to develop the probability distribution 438. In one example,the modeling engine 424 applies a regression model (such as a linear,quadratic or a Bayesian regression model) to the historic weather data436 to derive the probability distribution 438. The prediction servermay store the generated probability distributions 438 in the a weatherdatabase (such as the weather database 136 of FIG. 1) and/or aprobability database (not depicted).

FIG. 5 depicts a block diagram of predictively simulating the impact ofweather on the region and a corresponding simulation result. Moreparticularly, FIG. 5 depicts a simulation application 526 sampling oneor more probability distributions 538 (such as the probabilitydistributions 438 of FIG. 4) to execute a simulation of how weatherimpacts a region (such as the region 205 of FIG. 2). In someembodiments, the simulation application 526 is stored and executed by aclient device (such as the client device 110 of FIG. 1). In otherembodiments, the simulation application 526 is stored and executed by aprediction server (such as the prediction server 120 of FIG. 1). In yetother embodiments, a client device and a prediction server operate inconjunction with one another to execute the simulation application 526.

The simulation application 526 may be configured to accurately model howwater and/or wind acts upon the component properties of the region. Thesimulation application 526 may include a physics engine that isconfigured to replicate how objects act in the real world. In someembodiments, the physics engine may include a computation fluid dynamicsmodel that models how water flows across various surfaces and/ortopographies. That is, the simulation application 526 may be configuredto generate water objects in the simulation environment and determinehow the water flows through the region's topography as replicated byhigh-resolution models of the region.

As another example, the simulation application 526 may include a modelfor the component materials the of properties within the region, such asstructures, ground surfaces, vegetation, and/or sewage elements. Forexample, the simulation application 526 may model wood, steel, aluminum,vinyl, and/or other materials commonly associated with structures todetermine the structure's resistivity to wind and/or water. As anotherexample, the simulation application 526 may model how fast water flowsacross ground surface materials (e.g., asphalt, grass (and/or variousspecies thereof), dirt, etc.) and/or how much water can be absorbed bythe ground surface material. As still another example, the simulationapplication 526 may model a water drainage rate of a sewage element toaccurately simulate when a weather system produces more water than thesewage element can receive without flooding.

Prior to executing the simulation, the client device and/or theprediction server imports a virtual model of a region 540 (such as thevirtual model 340 of FIG. 3) into a simulation environment. The clientdevice and/or the prediction server may obtain the virtual model of theregion 540 from a model database (such as the model database 134 of FIG.1). In some embodiments, the simulation application 526 is configured toreceive an indication of the region (e.g., a geographic coordinate, anindication of a city/state, a zip code, etc.) from a user. The clientdevice and/or the prediction server then queries the model database toobtain the virtual model corresponding to the indication.

As illustrated, the client device and/or the prediction server obtainsthe probability distributions 538 corresponding to one or more inputparameters of the simulation application 526. In some embodiments, theprediction server and/or the client device may obtain the probabilitydistributions 538 directly from a weather database (such as the weatherdatabase 136 of FIG. 1) and/or a probability database. In otherembodiments where the simulation application is executed by a clientdevice, the client device may instruct the prediction server to obtainthe probability distributions 538 and transmit them to back to theclient device. After obtaining the probability distributions 538, theclient device and/or the prediction server randomly samples theprobability distributions 538 to determine the particular inputs for thesimulation.

The simulation application 526 then generates, in accordance with thesampled input parameters, a weather event and simulates the impact ofthe weather event as it acts upon the imported virtual model of theregion 540. According to aspects, the simulation application 526 modelshow the weather event moves across the simulation environment. As aresult, the weather event impacts portions of the simulation environmentdifferently. As one example, based on the physics engine and/orhistorical data, the weather event may weaken or strengthen as it movesacross the simulation environment, for example, based on the presence ofhills, valleys, and/or distance from a body of water.

In the illustrated embodiment, the simulation application 526 enablesthe user to view the impact of the weather event on the virtual model ofthe region 540. In this embodiment, the simulation application 526 maygenerate and present indicators 546 that indicate an impact of theweather event on corresponding models 542. Accordingly, if the userinteracts with an indicator 546, the indicator 546 may includeadditional details about the damage to the model 542, such as an extentof damage, a cost associated with the damage, a cause of the damage, anidentity of a damage component, and any other information describing thedamage to the model 542.

In the illustrated scenario, the simulated weather event caused a flood544 (for example, by overloading a retention pond as modeled by themodel 342 e of FIG. 3). As illustrated, the home modeled by a componentvirtual model 542 c is partially underwater. Accordingly, the model 542c is associated with an indicator 546 c that indicates the damage to themodel 542 c. Similarly, as illustrated, the home modeled by a componentvirtual model 542 a experienced damage to the roof. Accordingly, themodel 542 a is associated with an indicator 546 a that indicates thedamage to the model 542 a. Additionally, as illustrated, the homemodeled by a component virtual model 542 b experienced non-visibleerosive damage as determined based on the model of the materialsassociated with the model 542 b. Accordingly, the model 542 b isassociated with an indicator 546 b that indicates the damage to themodel 542 b.

It should be appreciated that the simulation application 526 may executethe simulation and automatically analyze the result without presenting avisual representation of the virtual model 540 and the correspondingindicators 546 to a user of the client device and/or the predictionserver. Accordingly, the simulation application 526 may be configuredexport the data associated with the indicators 546 to a simulationresult file that tabulates the impact of the weather event on thevarious properties. For example, the result file may be acomma-separated value (CSV) or extensible markup language (XML) filethat indicates an identifier and one or more values associated with thedamage (and/or lack thereof) for each property in the region.

Referring now to FIG. 6, illustrated is an example flow chart of anexample method 600 for predictively modeling risks associated withproperties within a region (such as the region 205 of FIG. 2). Asdescribed herein, the method 600 may be performed by a client device(such as the client device 110 of FIG. 1), a prediction server (such asthe prediction server 120 of FIG. 1), and/or a combination thereofexecuting a simulation application (such as the simulation application526 of FIG. 5). For ease of explanation, the following describes anembodiment where the method 600 is performed by a prediction server.That said, in alternative embodiments, some or all of the followingfunctionality may be performed by the client device.

The method 600 begins when the prediction server identifies a virtualmodel of the region (block 602). As described with respect to FIGS. 2and 3, the virtual model of the region may be a high-resolution digitaltwin of the region that models properties and/or the correspondingtopographies within the region to an accuracy of at least 10 cm. In someembodiments, the prediction server is configured to receive anindication of a particular property to analyze for potential risks ofdamage. For example, the prediction server may receive the indication ofthe particular property in response to a property owner applying for aninsurance product to cover the property. Accordingly, the predictionserver may identify geographic information associated with the indicatedproperty (e.g., an address or a geographic coordinate). In otherembodiments, the simulation application includes a map interface thatenables a user to select a particular geographic location to analyze forpotential risks of damage.

In response, the prediction server utilizes the geographic informationto query a model database (such as the model database 134 of FIG. 1) toobtain the virtual model of the region. In some embodiments, the regionis defined by a geographic radius centered about the geographicinformation. The radius may be a circular radius, a square radius, orany polygonal radius. In other embodiments, the region is defined by auser-selected region via the map interface. In yet other embodiments,the model database may store pre-segmented virtual models correspondingto a plurality of regions. For example, the model database may store avirtual model for a particular flood plain. In these embodiments, theprediction server may identify the virtual model in which the geographicinformation is located.

At block 604, the prediction server imports the identified virtual modelof the region into a simulation environment. The simulation environmentmay include a physics engine that includes computational fluid dynamicsthat accurately approximates how real world forces (e.g., gravity,friction, erosion) act upon a corresponding object (e.g., a structure orwater) in the real world. Upon importing the virtual model of theregion, the simulation application generates an accurate virtualrepresentation of the region, including the topography of the regionand/or the various materials that comprise the properties within theregion. In some embodiments, the simulation application is configured topresent a rendered visualization of the virtual model of the region. Inother embodiments, the simulation environment is not presented to a userof the prediction server.

At block 606, the prediction server sets one or more simulationparameters (e.g., a precipitation amount, a precipitation intensity, awind direction, a wind intensity, an indication of whether a type ofweather event to simulate (such as a tornado, a tropical storm, ahurricane, a cyclone, a typhoon, and/or a particular intensity and/orcategory thereof), and/or any other input weather parameter supported bythe simulation application) based on historic weather data. For example,as described with respect to FIG. 4, the prediction server utilize themodeling engine 424 to generate a plurality of probability distributions438 based on the historic weather data 436. Accordingly, the predictionserver may sample one or more of the plurality of probabilitydistributions to determine the value at which the simulation parameteris set.

At block 608, the prediction server executes a simulation in accordancewith the simulation parameters. That is, the prediction server causesthe simulation application to generate a simulated weather event thatacts upon the virtual model of the region. As described herein, thesimulation application includes a physics engine that accurate modelshow wind and/or precipitation acts upon specific portions of the regionbased on the specific materials and/or topographies of the propertieswithin the region. As a result, the simulation application models howthe weather event impacts a specific property as opposed to othersimilarly situated properties within the region.

In some embodiments, the prediction server executes a plurality ofsimulations. For example, the prediction server may be configured toexecute 100, 500, 1000, 5000, 10000, or even more simulations.Accordingly, prior to each simulation, the prediction server may samplea new set of simulation parameters based on the respective probabilitydistributions. For example, the prediction server may implement MonteCarlo sampling techniques and/or particle filtering techniques to samplethe probability distributions across the plurality of simulations. Byexecuting a plurality of simulations using Monte Carlo and/or particlefiltering sampling techniques, the prediction server simulates the mostlikely weather conditions a property of interest will experience. Insome embodiments, the state of the component virtual models are restoredto their original condition after each simulation (or after a fixednumber of simulations). In other embodiments, the output state of a mostrecent simulation is utilized as the input state for a next simulation.In these embodiments, the prediction server can predict the likelycondition of a particular property at a particular point in the future(e.g., 3 years, 5 years, 10 years, and/or a particular timeframeassociated with an insurance product) by setting the number ofconsecutive simulations to correspond to a number of days (and/or anumber of days historically associated with weather events within aspecific time period based on the historic weather data).

At block 610, the prediction server analyzes the result of thesimulation to determine a risk of damage to properties within thevirtual model of the region. More particularly, the prediction serveranalyzes the simulation result to determine whether component virtualmodel of a property included in the virtual model of the region wasdamaged during the simulation and/or an extent of damage thereto. Forexample, the simulation application may cause a particular property toflood due to being located proximate to a pooling point of water. Asanother example, the simulation application may cause another propertyto be damaged by a branch that broke off of a tree near the property. Asstill another example, the simulation application cause another propertyto experience roof damage due to hail. As yet another example, thesimulation application may cause another property to experience erosiondue to the various weather conditions. Of course, these are only exampletypes of damage that may be experienced by the component virtual modelsduring a simulation. The prediction server may be configured to analyzethe simulation result for any type of damage supported by the simulationapplication. Accordingly, the prediction server is capable of accuratelydetermining the different types of damage that are likely to occur toeach specific property.

In some embodiments, the prediction server is configured to parse theresult file to determine the damage level and/or type of damageexperienced by the component virtual models. If the prediction serverexecuted a plurality of simulations, the prediction server may beconfigured the simulation results for plurality of simulations todetermine the risk of damage. In Based on the type and/or extent ofdamage the component virtual model experiences during the simulation(s),the prediction server may generate a score indicative of a predictedrisk of damage of to the corresponding property. In some embodiments,the score is also based on a minimum level of weather severity requiredto cause damage to the property and a determined likelihood of thecorresponding weather severity actually acting upon the property.

Additionally or alternatively, the prediction server may present avisual interface of the simulation application that enables a user ofthe prediction server to view the simulation result (such as thesimulation result 540 of FIG. 5). In these embodiments, the simulationapplication may be configured to populate a plurality of overlays in thevisual interface detailing the level damage to the component virtualmodels and/or the generated scores. In some embodiments, the simulationapplication is configured to present an overlay that indicatesparticular regions that are likely to experience damage (such asflooding) and/or indicates properties that have scores within certainthresholds (based on absolute and/or relative scores).

At block 612, the prediction server is configured to update records in aproperty database to indicate the determined predicted risk of damage.As described herein, the prediction server may be operatively connectedto a property database (such as the property database 138 of FIG. 1)that maintains records for a plurality of properties. The predictionserver may be configured to associate a plurality of properties modeledby the virtual model of the region to a corresponding record associatedwith the property in the property database. Accordingly, the predictionserver may update the record corresponding to properties modeled by thevirtual model of the region to include the determined risk of damageand/or generated score. By updating the property database, the predictedrisk of damage can be synchronized with other computer systems thatutilize the risk of damage in performing their functionality. Forexample, one such computer system may relate to approving an insuranceapplication for underwriting and/or providing discounts on insuranceproducts associated with properties that have taken preventativemeasures to reduce a risk of damage. Another example computer system mayidentify preventative measures that can be undertaken to reduce acommunity's risks associated with weather events that are likely tooccur. It should be appreciated that the method 600 may includeadditional, fewer, or alternate actions, including those discussedelsewhere herein.

FIG. 7 illustrates a diagram of an example prediction server 720 (suchas the prediction server 120 of FIG. 1) in which the functionalities asdiscussed herein may be implemented. The prediction server 720 mayinclude one or more processors 722 as well as a memory 778. The memory778 may store an operating system 779 capable of facilitating thefunctionalities as described herein. The memory 778 may further imagedata 732, model data 734, historic weather data 736 and/or property data738, as described elsewhere herein. The prediction server 720 may alsostore a set of applications 775 (i.e., machine readable instructions).For example, one of the set of applications 775 may be a modeling engine724 configured to convert the historic weather data 736 into one or moreprobability distributions. As another example, another of the set ofapplications 775 may be a simulation application 726 configured tosimulate one or more weather events in a simulation environment basedupon a determined set of simulation inputs. It should be appreciatedthat other applications are envisioned.

The one or more processors 722 may interface with the memory 778 toexecute the operating system 779 and the set of applications 775. Thememory 778 may include one or more forms of volatile and/ornon-volatile, fixed and/or removable memory, such as read-only memory(ROM), electronic programmable read-only memory (EPROM), random accessmemory (RAM), erasable electronic programmable read-only memory(EEPROM), and/or other hard drives, flash memory, MicroSD cards, andothers.

The prediction server 720 may further include a communication module 777configured to communicate data via one or more networks 715. Accordingto some embodiments, the communication module 777 can include one ormore transceivers (e.g., WWAN, WLAN, and/or WPAN transceivers)functioning in accordance with IEEE standards, 3GPP standards, or otherstandards, and configured to receive and transmit data via one or moreexternal ports 776. For example, the communication module 777 mayreceive, via the network 715, image data representative of a region tostore with the image data 732 and/or to generate corresponding virtualmodels that are stored with the model data 734. The prediction server720 may further include a user interface 781 configured to presentinformation to the individual and/or receive inputs from a user. Asshown in FIG. 7, the user interface 781 may include a display screen 782and/or I/O components 783 (e.g., ports, capacitive or resistive touchsensitive input panels, keys, buttons, lights, LEDs, speakers,microphones). According to the present embodiments, the user may accessthe prediction server 720 via the user interface 781 to update theoperating system 779, execute one or more simulations using thesimulation application 726, and/or perform other functions. In someembodiments, the prediction server 720 may perform the functionalitiesas discussed herein as part of a “cloud” network or can otherwisecommunicate with other hardware or software components within the cloudto send, retrieve, or otherwise analyze data.

In general, a computer program product in accordance with an embodimentmay include a computer usable storage medium (e.g., standard randomaccess memory (RAM), an optical disc, a universal serial bus (USB)drive, or the like) having computer-readable program code embodiedtherein, wherein the computer-readable program code is adapted to beexecuted by the one or more processors 722 (e.g., working in connectionwith the operating system 779) to facilitate the functions as describedherein. In this regard, the program code may be implemented in anydesired language, and may be implemented as machine code, assembly code,byte code, interpretable source code or the like (e.g., via Python, orother languages, such as C, C++, Java, Actionscript, Objective-C,Javascript, CSS, XML). In some embodiments, the computer program productmay be part of a cloud network of resources.

ADDITIONAL CONSIDERATIONS

Although the preceding text sets forth a detailed description ofnumerous different embodiments, it should be understood that the legalscope of the invention is defined by the words of the claims set forthat the end of this patent. The detailed description is to be construedas exemplary only and does not describe every possible embodiment, asdescribing every possible embodiment would be impractical, if notimpossible. One could implement numerous alternate embodiments, usingeither current technology or technology developed after the filing dateof this patent, which would still fall within the scope of the claims.

It should also be understood that, unless a term is expressly defined inthis patent using the sentence “As used herein, the term ‘_(——————)’ ishereby defined to mean . . . ” or a similar sentence, there is no intentto limit the meaning of that term, either expressly or by implication,beyond its plain or ordinary meaning, and such term should not beinterpreted to be limited in scope based on any statement made in anysection of this patent (other than the language of the claims). To theextent that any term recited in the claims at the end of this patent isreferred to in this patent in a manner consistent with a single meaning,that is done for sake of clarity only so as to not confuse the reader,and it is not intended that such claim term be limited, by implicationor otherwise, to that single meaning.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Additionally, certain embodiments are described herein as includinglogic or a number of routines, subroutines, applications, orinstructions. These may constitute either software (code embodied on anon-transitory, tangible machine-readable medium) or hardware. Inhardware, the routines, etc., are tangible units capable of performingcertain operations and may be configured or arranged in a certainmanner. In example embodiments, one or more computer systems (e.g., astandalone, client or server computer system) or one or more hardwaremodules of a computer system (e.g., a processor or a group ofprocessors) may be configured by software (e.g., an application orapplication portion) as a hardware module that operates to performcertain operations as described herein.

In various embodiments, a hardware module may be implementedmechanically or electronically. For example, a hardware module maycomprise dedicated circuitry or logic that is permanently configured(e.g., as a special-purpose processor, such as a field programmable gatearray (FPGA) or an application-specific integrated circuit (ASIC) toperform certain operations. A hardware module may also compriseprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) that istemporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement a hardware modulemechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) may bedriven by cost and time considerations.

Accordingly, the term “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. Considering embodiments inwhich hardware modules are temporarily configured (e.g., programmed),each of the hardware modules need not be configured or instantiated atany one instance in time. For example, where the hardware modulescomprise a general-purpose processor configured using software, thegeneral-purpose processor may be configured as respective differenthardware modules at different times. Software may accordingly configurea processor, for example, to constitute a particular hardware module atone instance of time and to constitute a different hardware module at adifferent instance of time.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multipleof such hardware modules exist contemporaneously, communications may beachieved through signal transmission (e.g., over appropriate circuitsand buses) that connect the hardware modules. In embodiments in whichmultiple hardware modules are configured or instantiated at differenttimes, communications between such hardware modules may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware modules have access. Forexample, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods or routines described herein may be at leastpartially processor-implemented. For example, at least some of theoperations of a method may be performed by one or more processors orprocessor-implemented hardware modules. The performance of certain ofthe operations may be distributed among the one or more processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the one or more processors orprocessor-implemented modules may be located in a single geographiclocation (e.g., within a home environment, an office environment, or aserver farm). In other example embodiments, the one or more processorsor processor-implemented modules may be distributed across a number ofgeographic locations.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

As used herein any reference to “one embodiment” or “an embodiment”means that a particular element, feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneembodiment. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment.

Some embodiments may be described using the terms “coupled,”“connected,” “communicatively connected,” or “communicatively coupled,”along with their derivatives. These terms may refer to a direct physicalconnection or to an indirect (physical or communication) connection. Forexample, some embodiments may be described using the term “coupled” toindicate that two or more elements are in direct physical or electricalcontact. The term “coupled,” however, may also mean that two or moreelements are not in direct contact with each other, but yet stillco-operate or interact with each other. Unless expressly stated orrequired by the context of their use, the embodiments are not limited todirect connection.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive or and not to an exclusive or. For example,a condition A or B is satisfied by any one of the following: A is true(or present) and B is false (or not present), A is false (or notpresent) and B is true (or present), and both A and B are true (orpresent).

In addition, use of the “a” or “an” are employed to describe elementsand components of the embodiments herein. This is done merely forconvenience and to give a general sense of the description. Thisdescription, and the claims that follow, should be read to include oneor at least one and the singular also includes the plural unless thecontext clearly indicates otherwise.

This detailed description is to be construed as exemplary only and doesnot describe every possible embodiment, as describing every possibleembodiment would be impractical, if not impossible. One could implementnumerous alternate embodiments, using either current technology ortechnology developed after the filing date of this application.

Upon reading this disclosure, those of skill in the art will appreciatestill additional alternative structural and functional designs forsystem and a method for assigning mobile device data to a vehiclethrough the disclosed principles herein. Thus, while particularembodiments and applications have been illustrated and described, it isto be understood that the disclosed embodiments are not limited to theprecise construction and components disclosed herein. Variousmodifications, changes and variations, which will be apparent to thoseskilled in the art, may be made in the arrangement, operation anddetails of the method and apparatus disclosed herein without departingfrom the spirit and scope defined in the appended claims.

The particular features, structures, or characteristics of any specificembodiment may be combined in any suitable manner and in any suitablecombination with one or more other embodiments, including the use ofselected features without corresponding use of other features. Inaddition, many modifications may be made to adapt a particularapplication, situation or material to the essential scope and spirit ofthe present invention. It is to be understood that other variations andmodifications of the embodiments of the present invention described andillustrated herein are possible in light of the teachings herein and areto be considered part of the spirit and scope of the present invention.

Finally, the patent claims at the end of this patent application are notintended to be construed under 35 U.S.C. § 112(f), unless traditionalmeans-plus-function language is expressly recited, such as “means for”or “step for” language being explicitly recited in the claims. Thesystems and methods described herein are directed to an improvement tocomputer functionality, and improve the functioning of conventionalcomputers.

The invention claimed is:
 1. A computer-implemented method comprising:identifying, by one or more processors, a virtual model of a region, thevirtual model being generated based upon a plurality of color imagescaptured by one or more image sensors on one or more remote imagingvehicles, wherein the virtual model models the region to at least aminimum resolution and includes component virtual models of a pluralityof properties in the region; identifying, by the one or more processorsand based on color information from the plurality of color images, aplurality of features of the virtual model, wherein the plurality offeatures includes a vegetation type; importing, by the one or moreprocessors, the virtual model of the region into a simulationenvironment; obtaining, by the one or more processors, one or moreprobability distributions based on historical weather data associatedwith the region; determining, by the one or more processors and based onthe one or more probability distributions, a parameter value indicativeof a weather system associated with the region; executing, by the one ormore processors, a simulation in the simulation environment, wherein aspart of the simulation, the weather system characterized by theparameter value acts upon the virtual model of the region; analyzing, bythe one or more processors and using a predictive model, a result of thesimulation to determine a risk of damage to a component virtual model ofa property in the region during the simulation, wherein analyzing theresult includes entering at least one of the historical weather data andthe vegetation type as an input to the predictive model, wherein thecomponent virtual model includes a visual indication of damage; andupdating, by the one or more processors, a record corresponding to theproperty to indicate the risk of damage to the component virtual modelof the property during the simulation.
 2. The computer-implementedmethod of claim 1, further comprising: setting, by the one or moreprocessors, a parameter of one or more parameters indicative of at leastone of a precipitation amount, a precipitation intensity, a winddirection, a wind intensity, or an indication of whether a weather eventis occurring, wherein the weather event is at least one of a tornado, atropical storm, a cyclone, a typhoon, or a hurricane.
 3. Thecomputer-implemented method of claim 1, wherein executing the simulationcomprises: executing, by the one or more processors, a plurality ofsimulations.
 4. The computer-implemented method of claim 3, furthercomprising: prior to executing a particular simulation of the pluralityof simulations, sampling, by the one or more processors, the one or moreprobability distributions to determine a particular parameter value forthe particular simulation.
 5. The computer-implemented method of claim4, wherein sampling the one or more probability distributions comprises:executing, by the one or more processors, Monte Carlo samplingtechniques to determine the particular parameter value.
 6. Thecomputer-implemented method of claim 4, wherein obtaining the one ormore probability distributions comprises: receiving, via a communicationnetwork and from a prediction server, the one or more probabilitydistributions.
 7. The computer-implemented method of claim 3, furthercomprising: analyzing, by the one or more processors, the results of theplurality of simulations to determine a probability the virtual model ofthe property experiences one or more levels of damage.
 8. Thecomputer-implemented method of claim 7, wherein updating the recordcorresponding to the property to indicate the level of damage to thevirtual model of the property during the simulation comprises: updating,by the one or more processors, the record corresponding to the propertyto indicate the probability the virtual model of the propertyexperiences one or more levels of damage.
 9. The computer-implementedmethod of claim 8, further comprising: based on the probabilities thevirtual model of the property experiences one or more levels of damage,performing at least one of offering a discount on an insurance productsassociated with the property or approving an insurance productassociated with the property for underwriting.
 10. Thecomputer-implemented method of claim 1, wherein executing the simulationcomprises: identifying, by the one or more processors, the componentvirtual models of the plurality of properties in the region, wherein theplurality of properties include a ground surface or material,vegetation, a structure, a sewage element, or a topographical element;and associating, by the one or more processors, each property with amaterial and/or a model of how the property reacts when exposed to atleast one of wind or water.
 11. The computer-implemented method of claim10, wherein: a particular property is the structure; and the componentvirtual model of the property is determined based upon a buildinginformation modeling (BIM) model of the structure.
 12. Thecomputer-implemented method of claim 10, wherein executing thesimulation comprises: executing, by the one or more processors, acomputational fluid dynamics model to determine how water associatedwith the weather system flows through the virtual model of the region,wherein the computational fluid dynamics model analyzes the materialand/or model of the plurality of features of the virtual model whendetermining how the water flows through the virtual model of the region.13. A system comprising: one or more processors; one or moretransceivers operatively connected to the one or more processors andconfigured to send and receive communications over one or morecommunication networks; and one or more non-transitory memories coupledto the one or more processors and storing computer-executableinstructions, that, when executed by the one or more processors, causethe system to: identify a virtual model of a region, the virtual modelbeing generated based upon a plurality of color images captured by oneor more image sensors on one or more remote imaging vehicles, whereinthe virtual model models the region to at least a minimum resolution andincludes component virtual models of a plurality of properties in theregion; identify, based on color information from the plurality of colorimages, a plurality of features of the virtual model, wherein theplurality of features includes a vegetation type; import the virtualmodel of the region into a simulation environment; obtain one or moreprobability distributions based on historical weather data associatedwith the region; determine, based on the one or more probabilitydistributions, a parameter value indicative of a weather systemassociated with the region; execute a simulation in the simulationenvironment, wherein as part of the simulation, the weather systemcharacterized by the parameter value acts upon the virtual model of theregion; analyze, using a predictive model, a result of the simulation todetermine a risk of damage to a component virtual model of a property inthe region during the simulation, wherein analyzing the result includesentering at least one of the historical weather data and the vegetationtype as an input to the predictive model, wherein the component virtualmodel includes a visual indication of damage; and update a recordcorresponding to the property to indicate the risk of damage to thecomponent virtual model of the property during the simulation.
 14. Thesystem of claim 13, wherein the one or more non-transitory memoriescomprise: a first set of one or more memories storingcomputer-executable instructions at a client device; and a second set ofone or more memories storing computer-executable instructions at aprediction server.
 15. The system of claim 13, wherein to execute thesimulation, the instructions, when executed, cause the system to:present a visual representation of the virtual model for the simulation;and populate a plurality of overlays in the visual representationindicating damage levels to the component virtual models.
 16. The systemof claim 13, wherein to set the one or more parameters, theinstructions, when executed, cause the system to: execute a plurality ofsimulations; and prior to executing a particular simulation of theplurality of simulations, sample respective probability distributions todetermine a particular parameter value for the particular simulation.17. The system of claim 16, wherein to sample the respective probabilitydistributions, the instructions, when executed, cause the system to:execute Monte Carlo sampling techniques to determine the particularparameter value.
 18. The system of claim 13, wherein to execute thesimulation, the instructions, when executed, cause the system to:identify the component virtual models of the plurality of properties inthe region, wherein the plurality of properties includes a groundsurface or material, vegetation, a structure, a sewage element, or atopographical element; and associate each property with a materialand/or a model of how the property reacts when exposed to at least oneof wind or water.
 19. A non-transitory computer-readable medium storingcomputer-executable instructions, that, when executed by one or moreprocessors, cause one or more processors to: identify a virtual model ofa region, the virtual model being generated based upon a plurality ofcolor images captured by one or more image sensors on one or more remoteimaging vehicles, wherein the virtual model models the region to atleast a minimum resolution and includes component virtual models of aplurality of properties in the region; identify, based on colorinformation from the plurality of color images, a plurality of featuresof the virtual model, wherein the plurality of features includes avegetation type; import the virtual model of the region into asimulation environment; obtain one or more probability distributionsbased on historical weather data associated with the region; determine,based on the one or more probability distributions, a parameter valueindicative of a weather system associated with the region; execute asimulation in the simulation environment, wherein as part of thesimulation, the weather system characterized by the parameter value actsupon the virtual model of the region; analyze, using a predictive model,a result of the simulation to determine a risk of damage to a componentvirtual model of a property in the region during the simulation, whereinanalyzing the result includes entering at least one of the historicalweather data and the vegetation type as an input to the predictivemodel, wherein the component virtual model includes a visual indicationof damage; and update a record corresponding to the property to indicatethe risk of damage to the component virtual model of the property duringthe simulation.
 20. The system of claim 13, wherein: the propertyincludes a particular structure; and the component virtual model of theproperty is determined based upon a building information modeling (BIM)model of the particular structure indicating a material of an internalstructural component.